The field of mental chronometry has focused on the measurement of cognitive processes through the use of paradigms designed to isolate and quantify lower level elementary mental processes. Some of these elementary processes include visual scanning; stimuli discrimination, registration, and encoding; representational retrieval from memory; and speed or degree of automatization of learning. It is thought that every task requiring cognition involves three components: task relevant knowledge, problem solving strategies, and information processing efficiency. Together these components determine performance of behaviors. The degree of task-relevant knowledge may influence strategies that humans use to solve problems, as increasing amounts of knowledge lead to awareness of differing strategies and ways to use the knowledge. In addition, it has been demonstrated that familiarity with a task may not only influence how quickly the response is initiated, but also the quality and/or quantity of the executed task. An example of this dual influence is the demonstration that knowledge of a linguistic variable influences not only how quickly a verbal response is initiated, but also the duration of the pronunciation. It is possible to design studies to severely limit the influence of both declarative knowledge and cognitive strategies. This purposeful design allows the researcher to concentrate on the information processing component of the task. It is this part of any cognitive task that is investigated by chronometric researchers.
The investigation of basic information processes involves two outcome measures: reaction time and response accuracy. These two dependent dimensions produce measures of speed, accuracy, and speed-accuracy tradeoffs, however they do not allow inference concerning mental strategies used to produce overt responses or interpretation of how mental processes are temporally ordered. This field of research has been able to very precisely determine cognitive processing duration, yet it has been unable to definitively determine whether cognitive processing occurs in a strict serial manner, or whether different processes temporally overlap.
Traditional information processing models assumed that discrete cognitive processes occurred in a serial order, but that assumption has been replaced by a model of processing in which several processes occur simultaneously with a continuous flow of activation going from one process to another. It has been suggested that the number of cognitive processes differ from task to task and that response preparations most likely reflect task complexity. Further findings stemming from a lexical decision task support the postulate that information is accumulated gradually, consistent with the activation model of connectionist theorists. Regardless of the temporal congruity or incongruity of the timed cognitive processes, an important underlying assumption concerning measurement of cognitive processing ability is the premise that lower level mental processes, such as stimulus registration or identification, and response selection are completed prior to the onset of overt behavioral responses. Examples of chronometric paradigms are simple reaction time tasks (SRT), choice reaction time tasks (CRT), inspection time tasks (IT), and Posner's paradigm task. Simple reaction time tasks are often used as a baseline for stimulus registration, and a more complex task, such as a choice reaction task, is given along with an SRT. By subtracting the simple reaction time from the more complex choice task time, researchers have an index of decision time.
The Posner paradigm is especially relevant to the issue of reading, as this paradigm measures recall of letters and digits. The presupposition of this task is that the stimuli being recalled have familiar over-learned content, and consequently the latency of the response is not a measure of declarative knowledge, but a measure of how automatically one can identify the stimulus. Automaticity assumes that a cognitive skill has been over learned to the point that one is able to minimize conscious effort in the execution of the task.
Mental chronometry has been used to infer mental processes in reading and mathematics, as well as in other areas of educational interest. The Posner paradigm and similar tasks that commonly measure identification and retrieval of letters, digits, and words have been widely used in the study of reading.
Skill in recognizing words has been shown to be strongly related to the speed in which one acquires beginning reading proficiency. Predictably, the development of word recognition skills leads to increased reading comprehension.
Verbal retrieval can be used to measure the relative difference or similarity in the retrieval process of bilingual children, and can be used to measure general rates of cognitive processing, or specific rates of cognitive processing within a particular domain. Additionally, the study of naming can occur before a child is able to read, and therefore may prove to be an excellent predictor of beginning reading proficiency.
Two types of naming tasks have been studied in relation to reading: the continuous-list procedure, and the confrontation or discrete-trial tasks. A discrete-trial task is a procedure that asks the subject to respond with the identifying word as quickly as possible when a stimulus is flashed on a computer screen. This type of procedure differs from the continuous-list type procedure as it does not display stimuli typical of more ecologically valid naming tasks which include mental recognition and sequencing of the stimuli. Proponents of the continuous-list procedure suggest that the continuous-list type of task captures the very elements that naming and reading both share, specifically lexical retrieval that requires complex scanning, sequencing, and processing of continuously presented material. It has further been suggested that during the discrete-trial procedure, the only thing that can be measured is the speed with which one names a single stimulus, and this eliminates the opportunity to quantify serial and simultaneous processing that is so inherent in the act of reading.
Continuous-list rapid naming tasks have been shown to be powerful predictors of beginning reading ability. Specifically, rapid automatized naming has been shown to differentiate young normal readers from young dyslexic readers, and more specifically to distinguish dyslexia from other types of learning disabilities. Rapid automatized naming has also been shown to be an effective discriminator of dyslexic adolescent and adult students, to differentiate basic mathematical proficiency in learning disabled and non-disabled groups of children, and to differentiate right and left brain lesioned children. Further, an association between rapid automatized color naming and intelligence has been found for pre-reading girls.
One continuous-list procedure that captures the elements of reading in a non-reading task is the Rapid Automatized Naming Test (R.A.N.) R.A.N. was developed to detect subtle anomic qualities in children as well as to measure children's automatization skills requiring rapid serial verbal responses to common stimuli.
Originally R.A.N. was developed as nine different charts prepared to assess children's skill in verbal retrieval. Original sub-tests included: (1) Colors--red, green, black, blue, yellow (2) Numerals--2,6,9,4,7 (3) High Frequency Capital Letters--A, D, S, L, R, (4) Animals--dog, cat, cow, squirrel, bird (5) Lower Case Letters with low frequency "q"--b, q, e, c, i (6) Objects of Use--comb, key, watch, scissors, umbrella (7) Low Frequency Capital Letters--V, U, H, J, F (8)Random Objects--flag, drum, book, moon, wagon (9) Lower Case High Frequency Letters--p, o, d, a, s. Each chart had pictures of five different items repeated and arranged in random order to fill five horizontal rows of ten items each. The examiner familiarized the child with the items on each chart and then asked the child to name each item on the chart as quickly as possible. The child was timed on each chart and the resultant time indicated the score on the individual test.
There is evidence that the ability of the R.A.N. to identify differential reading ability is dependent upon the age at which the test is administered, the sub-test or sub-tests of the R.A.N. that are used, and the nature of the reading task that the R.A.N. is correlated with. It has been found that R.A.N. tasks adequately predict beginning reading, however correlation between naming speed and second grade reading was not as predictive as correlation at lower grade levels.
Another R.A.N. limitation is that the sub-tests are not equally predictive at differing ages. R.A.N. scores on the objects and colors sub-tests predict reading variance best for kindergarten subjects, however by first grade the letters sub-test consistently accounts for more reading variance than the other R.A.N. measures. This phenomenon is most likely due to the fact that the letters sub-test shares a source of declarative knowledge with the dependent reading measure. Interestingly, by the second grade, in the studies that used both R.A.N. letters and numbers to predict reading, the numbers task was found to predict reading slightly better than the letters task. This uneven prediction leads one to hypothesize that by second grade declarative knowledge predicts reading scores, however, the speed with which one manipulates abstract symbolic knowledge mediates that knowledge.
The usefulness of R.A.N. is limited because its sub-tests do not measure a solitary trait.
The test was designed to measure automaticity of word retrieval, but in reality this word retrieval measure consists of visual registration, sequencing and encoding of the stimulus, lexical retrieval, articulatory motor planning, and finally articulation of the stimulus. Further, the temporal organization of the cognitive processes involved in the task is still under debate. It is unclear as to whether word retrieval processes are discrete serially occurring processes or whether processes are better characterized as gradually activated with an undetermined amount of temporal overlap. The usual administration and timing of the R.A.N. does not allow one to definitively know which of the above factors truly accounts for the majority of the variance in correlation with reading. Sources of variation between individuals on this test include differences in cognitive processing time used for visual encoding or registration, access and retrieval of the verbal label, articulation duration, and interactions among the variables. Consequently, it is difficult to understand which R.A.N. factor really shares the most variance with reading.
The lack of clarity with respect to the sub-processes of the R.A.N. has been addressed by quantifying two behaviors. Articulation time and pause time as sub-process measures have been used in quantifying the portion of the total response time that each sub-process contributed. The division of the R.A.N. total response time into these two sub-processes allows calculation of the duration of the actual articulation time and the actual pause preparation time needed to register, encode, retrieve, and plan an articulation. Pause time is interpreted as the cognitive processing time that it takes an individual to ballistically trigger an overt response to the stimulus. In information processing terms, the pause time may be referred to as an index of the automatization of one's lexical access, verbal retrieval, or retrieval of phonological codes.
Thus, it would be highly desirable to provide methods and techniques that allow for the diagnosis of reading deficiencies by analyzing the pause and articulation times and correlating these times with a particular reading pattern. Such methods would be even more desirable if they were computerized so that subjects can interact with a machine. Computerized tools are particularly desirable for the flexibility and accuracy in their implementation and operation. Fully computerized analysis of pause and articulation times would not only provide more speed and accuracy in detecting and correcting reading deficiencies, but would also provide accessible tools that can be used by individuals having little or no expertise in cognitive processes. Computerized tools that can be operated by the subjects themselves with no or little supervision would offer a particularly attractive solution to present supervision intensive learning techniques.